111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
import logging
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from typing import Dict, Any, List, Union
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, ModelCapabilitiesEnum, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse
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# Configure logger
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logger = logging.getLogger(__name__)
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class AiInternal(BaseConnectorAi):
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"""Internal connector for document processing, generation, and rendering."""
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def __init__(self):
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super().__init__()
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logger.info("Internal Connector initialized")
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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "internal"
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def getModels(self) -> List[AiModel]:
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"""Get all available internal models."""
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return [
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AiModel(
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name="internal_extraction",
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displayName="Internal Document Extractor",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=8,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.TEXT_EXTRACTION],
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functionCall=self.extractDocument,
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priority=PriorityEnum.COST,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.EXTRACT],
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version="internal-extractor-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.001 + (bytesSent + bytesReceived) / (1024 * 1024) * 0.01
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),
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AiModel(
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name="internal_generation",
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displayName="Internal Document Generator",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=7,
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qualityRating=8,
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capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
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functionCall=self.generateDocument,
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priority=PriorityEnum.COST,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=[OperationTypeEnum.GENERATE],
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version="internal-generator-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.002 + (bytesReceived / (1024 * 1024)) * 0.005
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),
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AiModel(
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name="internal_rendering",
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displayName="Internal Document Renderer",
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connectorType="internal",
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maxTokens=0, # Not token-based
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contextLength=0,
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costPer1kTokensInput=0.0,
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costPer1kTokensOutput=0.0,
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speedRating=6,
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qualityRating=9,
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capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
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functionCall=self.renderDocument,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=[OperationTypeEnum.GENERATE],
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version="internal-renderer-v1",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.003 + (bytesReceived / (1024 * 1024)) * 0.008
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)
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]
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async def extractDocument(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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NOP - we only need the model for price calculations
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"""
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logger.error(f"Document extraction not to call here")
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return AiModelResponse(
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content="",
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success=False,
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error="Internal connector should not be called directly"
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)
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async def generateDocument(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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NOP - we only need the model for price calculations
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"""
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logger.error(f"Document generation not to call here")
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return AiModelResponse(
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content="",
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success=False,
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error="Internal connector should not be called directly"
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)
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async def renderDocument(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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NOP - we only need the model for price calculations
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"""
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logger.error(f"Document rendering not to call here")
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return AiModelResponse(
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content="",
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success=False,
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error="Internal connector should not be called directly"
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)
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